def reproj_distro():
local = get_local()
data = get_edges()
r2 = redis.StrictRedis(port=6385, decode_responses=True)
for tbin in [(2, 4), (2, 2), (4, 4), (4, 2), (4, 1), (3, 1)]:
print('Processing:', tbin)
tkey = '%d_%d' % tbin
# Get Kernel
kpca_key = 'subspace:pca:kernel:' + tkey
kpca = PCAnalyzer.load(r2, kpca_key)
# Get Training Data
data_raw = r2.lrange('subspace:pca:' + tkey, 0, -1)
pca_pts = np.array([np.fromstring(x) for x in data_raw])
kdtree = KDTree(200, maxdepth=8, data=pca_pts, method='middle')
proj_pts = kpca.project(alpha.xyz)
biased_hcubes = []
for i, pt in enumerate(proj_pts):
biased_hcubes.append(kdtree.probe(pt, probedepth=9))
if len(data) == 0:
print('No Raw PCA data points for bin %s.... Going to next bin',
str(tbin))
continue
counts = {}
for i in biased_hcubes:
if i not in counts:
counts[i] = 0
counts[i] += 1
for i in local[tkey]['keys']:
if i not in counts:
counts[i] = 0
print('check')
cvect = [counts[i] for i in local[tkey]['keys']]
d = np.array(cvect) / sum(cvect)
c = np.array(data[tkey])
lcnt = np.sum(c, axis=0)
gcnt = np.sum(c, axis=1)
norm = np.nan_to_num(c / np.linalg.norm(c, axis=-1)[:, np.newaxis])
# Add biased data as a col
kpca_cnt = np.array([int(i) for i in local[tkey]['count']])
kpca_cnt_norm = kpca_cnt / np.sum(kpca_cnt)
arr = np.vstack((norm, kpca_cnt_norm, d)).T
rowlist = tuple(gcnt) + (
'localKPCA',
'biased',
)
P.bargraph((np.mean(norm, axis=0), d), tkey, ['Reweight', 'Biased'])
def reproj_distro():
local = get_local()
data = get_edges()
r2 = redis.StrictRedis(port=6385, decode_responses=True)
for tbin in [(2,4), (2,2), (4,4), (4,2), (4,1), (3,1)]:
print('Processing:', tbin)
tkey = '%d_%d' % tbin
# Get Kernel
kpca_key = 'subspace:pca:kernel:' + tkey
kpca = PCAnalyzer.load(r2, kpca_key)
# Get Training Data
data_raw = r2.lrange('subspace:pca:' + tkey, 0, -1)
pca_pts = np.array([np.fromstring(x) for x in data_raw])
kdtree = KDTree(200, maxdepth=8, data=pca_pts, method='middle')
proj_pts = kpca.project(alpha.xyz)
biased_hcubes = []
for i, pt in enumerate(proj_pts):
biased_hcubes.append(kdtree.probe(pt, probedepth=9))
if len(data) == 0:
print('No Raw PCA data points for bin %s.... Going to next bin', str(tbin))
continue
counts = {}
for i in biased_hcubes:
if i not in counts:
counts[i] = 0
counts[i] += 1
for i in local[tkey]['keys']:
if i not in counts:
counts[i] = 0
print('check')
cvect = [counts[i] for i in local[tkey]['keys']]
d = np.array(cvect)/sum(cvect)
c = np.array(data[tkey])
lcnt = np.sum(c, axis=0)
gcnt = np.sum(c, axis=1)
norm = np.nan_to_num(c / np.linalg.norm(c, axis=-1)[:, np.newaxis])
# Add biased data as a col
kpca_cnt = np.array([int(i) for i in local[tkey]['count']])
kpca_cnt_norm = kpca_cnt / np.sum(kpca_cnt)
arr = np.vstack((norm, kpca_cnt_norm, d)).T
rowlist = tuple(gcnt) + ('localKPCA', 'biased',)
P.bargraph((np.mean(norm, axis=0), d), tkey, ['Reweight', 'Biased'])
def execute(self, job):
# PRE-PREOCESS ----------------------------------------------------------
settings = systemsettings()
bench = microbench('sim_%s' % settings.name, self.seqNumFromID())
bench.start()
stat = StatCollector('sim_%s' % settings.name, self.seqNumFromID())
mylogical_seqnum = str(self.seqNumFromID())
# Prepare working directory, input/output files
conFile = os.path.join(job['workdir'], job['name'] + '.conf')
logFile = conFile.replace('conf', 'log') # log in same place as config file
dcdFile = conFile.replace('conf', 'dcd') # dcd in same place as config file
USE_SHM = True
ADAPTIVE_CENTROID = False
SIMULATE_RATIO = settings.SIMULATE_RATIO
if SIMULATE_RATIO > 1:
logging.warning(" USING SIMULATION RATIO OF %d -- THis is ONLY for debugging", SIMULATE_RATIO)
frame_size = (SIMULATE_RATIO * int(job['interval'])) / (1000)
logging.info('Frame Size is %f Using Sim Ratio of 1:%d', \
frame_size, SIMULATE_RATIO)
EXPERIMENT_NUMBER = settings.EXPERIMENT_NUMBER
logging.info('Running Experiment Configuration #%d', EXPERIMENT_NUMBER)
# TODO: FOR LINEAGE
# srcA, srcB = eval(job['src_bin'])
# stat.collect('src_bin', [str(srcA), str(srcB)])
traj = None
# EXECUTE SIMULATION ---------------------------------------------------------
if self.skip_simulation:
logging.info('1. SKIPPING SIMULATION.....')
USE_SHM = False
job['dcd'] = dcdFile
key = wrapKey('jc', job['name'])
self.data[key]['dcd'] = dcdFile
else:
logging.info('1. Run Simulation')
# Prepare & source to config file
with open(self.data['sim_conf_template'], 'r') as template:
source = template.read()
# >>>>Storing DCD into shared memory on this node
if USE_SHM:
# ramdisk = '/dev/shm/out/'
ramdisk = '/tmp/ddc/'
if not os.path.exists(ramdisk):
os.mkdir(ramdisk)
job['outputloc'] = ramdisk
dcd_ramfile = os.path.join(ramdisk, job['name'] + '.dcd')
else:
job['outputloc'] = ''
with open(conFile, 'w') as sysconfig:
sysconfig.write(source % job)
logging.info("Config written to: " + conFile)
# # Run simulation in parallel
# if 'parallel' in job:
# numnodes = job['parallel']
# total_tasks = numnodes * 24
# cmd = 'mpiexec -n %d namd2 %s > %s' % (total_tasks, conFile, logFile)
# # Run simulation single threaded
# else:
# cmd = 'namd2 %s > %s' % (conFile, logFile)
# cmd = 'mpirun -n %d namd2 %s > %s' % (PARALLELISM, conFile, logFile)
check = executecmd('module list')
logging.debug('%s', check)
cmd = 'namd2 +p%d %s > %s' % (PARALLELISM, conFile, logFile)
# MICROBENCH #1 (file to Lustre)
# logging.debug("Executing Simulation:\n %s\n", cmd)
# bench = microbench()
# bench.start()
# stdout = executecmd(cmd)
# logging.info("SIMULATION Complete! STDOUT/ERR Follows:")
# bench.mark('SimExec:%s' % job['name'])
# shm_contents = os.listdir('/dev/shm/out')
# logging.debug('Ramdisk contents (should have files) : %s', str(shm_contents))
# shutil.copy(ramdisk + job['name'] + '.dcd', job['workdir'])
# logging.info("Copy Complete to Lustre.")
# bench.mark('CopyLustre:%s' % job['name'])
# shutil.rmtree(ramdisk)
# shm_contents = os.listdir('/dev/shm')
# logging.debug('Ramdisk contents (should be empty) : %s', str(shm_contents))
# bench.show()
max_expected_obs = int(job['runtime']) // int(job['dcdfreq'])
# Retry upto 3 attempts if the sim fails
MAX_TRY = 3
for i in range(MAX_TRY, 0, -1):
min_required_obs = int(max_expected_obs * ((i-1)/(MAX_TRY)))
logging.debug("Executing Simulation:\n %s\n", cmd)
logging.debug('# Obs Expected to see: %d', max_expected_obs)
stdout = executecmd(cmd)
logging.info("SIMULATION Complete! STDOUT/ERR Follows:")
# Check file for expected data
if USE_SHM:
traj = md.load(dcd_ramfile, top=job['pdb'])
else:
traj = md.load(dcdFile, top=job['pdb'])
logging.info("Obs Threshold = %4d", min_required_obs)
logging.info("#Obs This Traj = %4d", traj.n_frames)
if traj.n_frames >= min_required_obs:
logging.info('Full (enough) Sim Completed')
break
logging.info('Detected a failed Simulation. Retrying the same sim.')
bench.mark('SimExec:%s' % job['name'])
# Internal stats
sim_length = self.data['sim_step_size'] * int(job['runtime'])
sim_realtime = bench.delta_last()
sim_run_ratio = (sim_realtime/60) / (sim_length/1000000)
logging.info('##SIM_RATIO %6.3f min-per-ns-sim', sim_run_ratio)
stat.collect('sim_ratio', sim_run_ratio)
if USE_SHM:
shm_contents = os.listdir(ramdisk)
logging.debug('Ramdisk contents (should have files) : %s', str(shm_contents))
if not os.path.exists(dcd_ramfile):
logging.warning("DCD FILE NOT FOUND!!!! Wait 10 seconds for sim to close it (???)")
time.sleep(10)
if not os.path.exists(dcd_ramfile):
logging.warning("DCD STIILL FILE NOT FOUND!!!!")
else:
logging.info("DCD File was found")
# # MICROBENCH #2 (file to Alluxio)
# allux = AlluxioClient()
# # copy to Aluxio FS
# allux.put(ramdisk + job['name'] + '.dcd', '/')
# logging.info("Copy Complete to Alluxio.")
# bench.mark('CopyAllux:%s' % job['name'])
# And copy to Lustre
# shutil.copy(ramdisk + job['name'] + '.dcd', job['workdir'])
# And copy to Lustre (usng zero-copy):
if USE_SHM:
src = open(dcd_ramfile, 'rb')
dest = open(dcdFile, 'w+b')
offset = 0
dcdfilesize = os.path.getsize(dcd_ramfile)
while True:
sent = sendfile(dest.fileno(), src.fileno(), offset, dcdfilesize)
if sent == 0:
break
offset += sent
logging.info("Copy Complete to Lustre.")
bench.mark('CopyLustre:%s' % job['name'])
# TODO: Update job's metadata
key = wrapKey('jc', job['name'])
self.data[key]['dcd'] = dcdFile
# ANALYSIS ------- ---------------------------------------------------------
# ANALYSIS ALGORITHM
# 1. With combined Sim-analysis: file is loaded locally from shared mem
logging.debug("2. Load DCD")
# Load full higher dim trajectory
# traj = datareduce.filter_heavy(dcd_ramfile, job['pdb'])
if traj is None:
if USE_SHM:
traj = md.load(dcd_ramfile, top=job['pdb'])
else:
traj = md.load(dcdFile, top=job['pdb'])
# Center Coordinates
traj.center_coordinates()
bench.mark('File_Load')
logging.debug('Trajectory Loaded: %s (%s)', job['name'], str(traj))
# DIMENSIONALITY REDUCTION --------------------------------------------------
# 4-A. Subspace Calcuation: RMS using Alpha-Filter
#------ A: RMSD-ALPHA ------------------
# S_A = rmslist
logging.info('---- RMSD Calculation against pre-defined centroids ----')
# RMSD is calculated on the Ca ('alpha') atoms in distance space
# whereby all pairwise distances are calculated for each frame.
# Pairwise distances are plotted in euclidean space
# Distance to each of the 5 pre-calculated centroids is calculated
# 1. Filter to Alpha atoms
alpha = traj.atom_slice(deshaw.FILTER['alpha'])
# 2. (IF USED) Convert to distance space: pairwise dist for all atom combinations
# alpha_dist = dr.distance_space(alpha)
# 3. Calc RMS for each conform to all centroids
# Heuristic centroid weight (TODO: make this trained)\
# 4. For adaptive Centriods
# Centroids Will be pulled & updated.
logging.info('CENTROID Retrieval & Updating')
self.wait_catalog()
# If they were mutable....
# logging.info('Acquiring a Lock on the Centroids')
# centroids = self.catalog.loadNPArray('centroid')
# thetas = self.catalog.loadNPArray('thetas')
# lock = self.catalog.lock_acquire('centroid')
# if lock is None:
# logging.info('Could not lock the Centroids. Will use current cached (possibly stale) data.')
# bench.mark('ConcurrLockCentroid'%(A,B))
# Implemented as a Transactional Data Structure....
if ADAPTIVE_CENTROID:
centroids = []
for state in range(numLabels):
cent_raw = self.catalog.lrange('centroid:xyz:%d'%state, 0, -1)
cent_xyz = [pickle.loads(i) for i in cent_raw]
cent_npts = [int(i) for i in self.catalog.lrange('centroid:npts:%d'%state, 0, -1)]
c_sum = np.zeros(shape=cent_xyz[0].shape)
c_tot = 0
for x, n in zip(cent_xyz, cent_npts):
c = x * n
c_sum += c
c_tot += n
centroids.append(c_sum / c_tot)
else:
centroids = self.catalog.loadNPArray('centroid')
# if EXPERIMENT_NUMBER < 10:
# 5. Calculate the RMSD for each filtered point to 5 pre-determined centroids
# cw = [.92, .94, .96, .99, .99]
cw = [.94, .95, .97, .99, .99]
numLabels = len(self.data['centroid'])
numConf = len(traj.xyz)
stat.collect('numpts',numConf)
# 4. Account for noise : Simple spatial mean filter over a small window
# Where size of window captures extent of noise
# (e.g. 10000fs window => motions under 10ps are considered "noisy")
noise = self.data['obs_noise']
stepsize = 500 if 'interval' not in job else int(job['interval'])
nwidth = noise//(2*stepsize)
noisefilt = lambda x, i: np.mean(x[max(0,i-nwidth):min(i+nwidth, len(x))], axis=0)
rms_filtered = np.array([noisefilt(alpha.xyz, i) for i in range(numConf)])
# Notes: Delta_S == rmslist
rmslist_sv = calc_rmsd(rms_filtered, centroids, weights=cw)
# rmslist = adaptive_rmsd(rms_filtered, centroids, theta)
# else:
rmslist = calc_rmsd(alpha, centroids)
numConf = traj.n_frames
numLabels = len(centroids)
# rmslist = calc_rmsd(alpha.xyz, self.data['centroid'], weights=cw)
logging.debug(' RMS: %d points projected to %d centroid-distances', \
numConf, numLabels)
# 6. Apply Heuristics Labeling -- Single Variate
rmslabel = []
binlist = [(a, b) for a in range(numLabels) for b in range(numLabels)]
label_count = {ab: 0 for ab in binlist}
groupbystate = [[] for i in range(numLabels)]
groupbybin = {ab: [] for ab in binlist}
for i, rms in enumerate(rmslist_sv):
# Sort RMSD by proximity & set state A as nearest state's centroid
A, B = np.argsort(rms)[:2]
# Calc Absolute proximity between nearest 2 states' centroids
# THETA Calc derived from static run. it is based from the average std dev of all rms's from a static run
# of BPTI without solvent. It could be dynamically calculated, but is hard coded here
# The theta is divided by four based on the analysis of DEShaw:
# est based on ~3% of DEShaw data in transition (hence )
# avg_stddev = 0.34119404492089034
# theta = settings.RMSD_THETA
## FOR ADAPTIVE Cantroids. Theta is now updated dyamically
# NOTE: Original formulate was relative. Retained here for reference:
# Rel vs Abs: Calc relative proximity for top 2 nearest centroids
# relproximity = rms[A] / (rms[A] + rms[rs[1]])
# B = rs[1] if relproximity > (.5 - theta) else A
# proximity = abs(rms[prox[1]] - rms[A]) / (rms[prox[1]] + rms[A]) #relative
#proximity = abs(rms[prox[1]] - rms[A]) #abs
# Update for Adaptive Centroid.
delta = np.abs(rms[B] - rms[A])
# (TODO: Factor in more than top 2, better noise)
# Label secondary sub-state
# sub_state = B prox[1] if proximity < theta else A
# For ADAPTIVE Centroids
if delta < 0.33:
sub_state = B
else:
sub_state = A
rmslabel.append((A, sub_state))
# Add this index to the set of indices for this respective label
# TODO: Should we evict if binsize is too big???
# logging.debug('Label for observation #%3d: %s', i, str((A, B)))
label_count[(A, sub_state)] += 1
# Group high-dim point by state
# TODO: Consider grouping by stateonly or well/transitions (5 vs 10 bins)
groupbystate[A].append(i)
groupbybin[(A, sub_state)].append(i)
# stat.collect('observe', label_count)
bench.mark('RMS')
logging.info('Labeled the following:')
for A in range(numLabels):
if len(groupbystate[A]) > 0:
logging.info('label,state,%d,num,%d', A, len(groupbystate[A]))
for ab in binlist:
if len(groupbybin[ab]) > 0:
A, B = ab
logging.info('label,bin,%d,%d,num,%d', A, B, len(groupbybin[ab]))
# FEATURE LANDSCAPE -- Multi-Variate
# Calc Feature landscape for each frame's RMSD
feal_list = [feal.atemporal(rms) for rms in rmslist]
logging.info('Calculated Feature Landscape. Aggregate for this traj')
# For logging purposes
agg_feal = np.mean(feal_list, axis=0)
logging.info('CountsMax [C]: %s', str(agg_feal[:5]))
logging.info('StateDist [S]: %s', str(agg_feal[5:10]))
logging.info('RelDist [A-B]: %s', str(agg_feal[10:]))
# ADAPTIVE CENTROID & THETA CALCULATION
# if lock is None:
# logging.info('Never acqiured a lock. Skipping adaptive update (TODO: Mark pts as stale)')
# else:
# logging.info('Updating Adaptive Centroid')
if ADAPTIVE_CENTROID:
pipe = self.catalog.pipeline()
for state in range(numLabels):
n_pts = len(groupbybin[(state, state)])
if n_pts == 0:
logging.info('Skipping State %d Centroid -- Well not visited on this trajectory')
continue
cent_xyz = [alpha.xyz[i] for i in groupbybin[(state, state)]]
cent_npts = len(groupbybin[(state, state)])
c_sum = np.zeros(shape=alpha.xyz[0].shape)
for pt in cent_xyz:
c_sum += pt
centroid_local = c_sum / n_pts
centroid_delta = LA.norm(centroids[state] - cent)
pipe.rpush('centroid:xyz:%d' % state, pickle.dumps(centroid_local))
pipe.rpush('centroid:npts:%d' % state, n_pts)
pipe.rpush('centroid:delta:%d' % state, centroid_delta)
pipe.execute()
# 4-B. Subspace Calcuation: COVARIANCE Matrix, 200ns windows, Full Protein
#------ B: Covariance Matrix -----------------
if EXPERIMENT_NUMBER > 5:
# 1. Project Pt to PC's for each conform (top 3 PC's)
logging.info('---- Covariance Calculation on 200ns windows (Full Protein, cartesian Space) ----')
# Calculate Covariance over 200 ps Windows sliding every 100ps
# These could be user influenced...
WIN_SIZE_NS = .2
SLIDE_AMT_NS = .1
logging.debug("Calculating Covariance over trajectory. frame_size = %.1f, WINSIZE = %dps, Slide = %dps",
frame_size, WIN_SIZE_NS*1000, SLIDE_AMT_NS*1000)
covar = dr.calc_covar(alpha.xyz, WIN_SIZE_NS, frame_size, slide=SLIDE_AMT_NS)
bench.mark('CalcCovar')
stat.collect('numcovar', len(covar))
logging.debug("Calcualted %d covariance matrices. Storing variances", len(covar))
# BARRIER: WRITE TO CATALOG HERE -- Ensure Catalog is available
# try:
self.wait_catalog()
# except OverlayNotAvailable as e:
# logging.warning("Catalog Overlay Service is not available. Scheduling ASYNC Analysis")
# Update Catalog with 1 Long Atomic Transaction
global_index = []
with self.catalog.pipeline() as pipe:
while True:
try:
logging.debug('Update Filelist')
pipe.watch(wrapKey('jc', job['name']))
file_idx = pipe.rpush('xid:filelist', job['dcd']) - 1
# HD Points
logging.debug('Update HD Points')
for x in range(traj.n_frames):
# Note: Pipelined insertions "should" return contiguous set of index points
index = pipe.rpush('xid:reference', (file_idx, x)) - 1
global_index.append(index - 1)
pipe.multi()
logging.debug('Update RMS Subspace')
for x in range(traj.n_frames):
A, B = rmslabel[x]
index = global_index[x]
# Labeled Observation (from RMSD)
pipe.rpush('label:rms', rmslabel[x])
pipe.rpush('varbin:rms:%d_%d' % (A, B), index)
# pipe.rpush('lineage:rms:%d_%d:%d_%d' % (srcA, srcB, A, B), index)
# pipe.rpush('lineage:pca:%s:%d_%d' % (job['src_hcube'], A, B), index)
pipe.rpush('subspace:rms', bytes(rmslist_sv[x]))
pipe.rpush('subspace:feal', bytes(feal_list[x]))
logging.debug('Update OBS Counts')
for b in binlist:
pipe.rpush('observe:rms:%d_%d' % b, label_count[b])
pipe.incr('observe:count')
pipe.hset('anl_sequence', job['name'], mylogical_seqnum)
if EXPERIMENT_NUMBER > 5:
logging.debug('Update Covar Subspace')
for i, si in enumerate(covar):
logging.debug('Update COVAR Pt #%d', i)
local_index = int(i * frame_size * SLIDE_AMT_NS)
pipe.rpush('subspace:covar:pts', bytes(si))
pipe.rpush('subspace:covar:xid', global_index[local_index])
pipe.rpush('subspace:covar:fidx', (file_idx, local_index))
logging.debug('Executing')
pipe.execute()
break
except redis.WatchError as e:
logging.debug('WATCH ERROR')
continue
self.data[key]['xid:start'] = global_index[0]
self.data[key]['xid:end'] = global_index[-1]
bench.mark('Indx_Update')
# (Should we Checkpoint here?)
# 4-C. Subspace Calcuation: PCA BY Strata (PER STATE) using Alpha Filter
#------ C: GLOBAL PCA by state -----------------
# Update PCA Vectors for each state with new data
if EXPERIMENT_NUMBER > 5 and EXPERIMENT_NUMBER < 10:
logging.info('---- PCA per BIN over Alpha Filter in cartesian Space ----')
# TODO: This will eventually get moved into a User process macrothread
# which will set in between analysis and controller.
# For now, we're recalculating using a lock
# Check if vectors need to be recalculated
# Connect to reservoir samples
# TODO: Catalog or Cache?
reservoir = ReservoirSample('rms', self.catalog)
# STALENESS_FACTOR = .25 # Recent updates account for 25% of the sample (Just a guess)
num_inserted = {ab: 0 for ab in binlist}
num_params = np.prod(alpha.xyz.shape[1:])
for A, B in binlist:
num_observations = len(groupbybin[(A,B)])
if num_observations == 0:
logging.info('No data received for bin (%d,%d). Not processing this bin here.', A, B)
continue
res_label = '%d_%d' % (A,B)
updateVectors = False
kpca_key = 'subspace:pca:kernel:%d_%d' % (A, B)
kpca = PCAnalyzer.load(self.catalog, kpca_key)
newkpca = False
if kpca is None:
# kpca = PCAKernel(None, 'sigmoid')
kpca = PCAKernel(6, 'rbf')
newkpca = True
logging.info('PCA: Checking if current vectors for state %d are out of date', A)
rsize = reservoir.getsize(res_label)
tsize = kpca.trainsize
# KPCA is out of date is the sample size is 20% larger than previously used set
# Heuristics --- this could be a different "staleness" factor or we can check it some other way
if newkpca or rsize > (tsize * 1.5):
# Should we only use a sample here??? (not now -- perhaps with larger rervoirs or if KPCA is slow
traindata = reservoir.get(res_label)
if newkpca:
logging.info('New PCA Kernel. Trained on data set of size %d. Current \
reservoir is %d pts.', tsize, rsize)
logging.info('Projecting %d points on Kernel PCA for bin (%d,%d)',
num_observations, A, B)
traindata = np.zeros(shape=((num_observations,)+alpha.xyz.shape[1:]),
dtype=np.float32)
for i, index in enumerate(groupbybin[(A,B)]):
np.copyto(traindata[i], alpha.xyz[index])
else:
logging.info('PCA Kernel is old (Updating it). Trained on data set of \
size %d. Current reservoir is %d pts.', tsize, rsize)
if len(traindata) <= num_params:
logging.info("Not enough data to calculate PC's (Need at least %d \
observations). Skipping PCA for Bin (%d,%d)", num_params, A, B)
hd_pts = np.zeros(shape=((num_observations,)+alpha.xyz.shape[1:]), dtype=np.float32)
for i, index in enumerate(groupbybin[(A,B)]):
np.copyto(hd_pts[i], alpha.xyz[index])
num_inserted[(A,B)] = reservoir.insert(res_label, hd_pts)
logging.debug('Updating reservoir Sample for Bin (%d, %d)')
continue
logging.info(' Performing Kernel PCA (Gaussian) for bin (%d,%d) using traindata of size %d', \
A, B, len(traindata))
kpca.solve(traindata)
# NOTE: Pick PCA Algorithm HERE
# pca = calc_kpca(np.array(traindata), kerneltype='sigmoid')
# pca = calc_pca(np.array(traindata))
bench.mark('CalcKPCA_%d_%d'%(A,B))
# new_vect = pca.alphas_.T
lock = self.catalog.lock_acquire(kpca_key)
if lock is None:
logging.info('Could not lock the PC Kernel for Bin (%d,%d). Not updating', A, B)
else:
kpca.store(self.catalog, kpca_key)
lock = self.catalog.lock_release(kpca_key, lock)
bench.mark('ConcurrPCAWrite_%d_%d'%(A,B))
# Project Reservoir Sample to the Kernel and overwrite current set of points
# This should only happen up until the reservior is filled
# If we are approx above to train, be sure to project all reservor points
if not newkpca:
logging.info('Clearing and Re-Projecting the entire reservoir of %d points for Bin (%d,%d).', \
rsize, A, B)
rsamp_lowdim = kpca.project(traindata)
pipe = self.catalog.pipeline()
pipe.delete('subspace:pca:%d_%d'%(A,B))
for si in rsamp_lowdim:
pipe.rpush('subspace:pca:%d_%d'%(A,B), bytes(si))
pipe.execute()
else:
logging.info('PCA Kernel is good -- no need to change them')
bench.mark('start_ProjPCA')
logging.info('Projecting %d points on Kernel PCA for Bin (%d,%d)', num_observations, A, B)
hd_pts = np.zeros(shape=((num_observations,)+alpha.xyz.shape[1:]), dtype=np.float32)
for i, index in enumerate(groupbybin[(A,B)]):
np.copyto(hd_pts[i], alpha.xyz[index])
pc_proj = kpca.project(hd_pts)
bench.mark('ProjPCA_%d_%d'%(A,B))
# 2. Append subspace in catalog
pipe = self.catalog.pipeline()
for si in pc_proj:
pipe.rpush('subspace:pca:%d_%d' % (A,B), bytes(si))
pipe.execute()
logging.debug('Updating reservoir Sample')
num_inserted[(A,B)] = reservoir.insert(res_label, hd_pts)
bench.mark('PCA')
pipe = self.catalog.pipeline()
for ab, num in num_inserted.items():
if num > 0:
pipe.rpush('subspace:pca:updates:%d_%d' % (A, B), num)
pipe.execute()
# ---- POST PROCESSING
if USE_SHM:
shutil.rmtree(ramdisk)
# shm_contents = os.listdir('/dev/shm')
shm_contents = os.listdir('/tmp')
logging.debug('Ramdisk contents (should be empty of DDC) : %s', str(shm_contents))
# For benchmarching:
# print('##', job['name'], dcdfilesize/(1024*1024*1024), traj.n_frames)
bench.show()
stat.show()
# Return # of observations (frames) processed
return [numConf]
def execute(self):
"""Special execute function for the reweight operator -- check/validate.
"""
# PRE-PROCESSING ---------------------------------------------------------------------------------
logging.debug(
"============================ <PRE-PROCESS> ============================="
)
self.cacheclient = CacheClient(self.name)
numLabels = 5
binlist = [(A, B) for A in range(numLabels) for B in range(numLabels)]
labeled_pts_rms = self.catalog.lrange('label:rms', 0, -1)
num_pts = len(labeled_pts_rms)
logging.debug('##NUM_OBS: %d', num_pts)
# TEST_TBIN = [(i,j) for i in range(2,5) for j in range(5)]
TEST_TBIN = [(2, 0), (4, 2), (2, 2), (4, 1), (3, 1), (4, 4), (0, 4),
(0, 2), (0, 1)]
MAX_SAMPLE_SIZE = 100 # Max # of cov traj to back project per HCube
MAX_PT_PER_MATRIX = 100 # Num points to sample from each cov traj
COVAR_SIZE = 200 # Ea Cov "pt" is 200 HD pts. -- should be static based on user query
MAX_HCUBE = 6 # Max Num HCubes to process
# IMPLEMENT USER QUERY with REWEIGHTING:
logging.debug(
"======================= <QUERY PROCESSING> ========================="
)
# 1. RUN KPCA on <<?????>> (sample set) and project all pts
# 2. Calculate K-D Tree on above
# 3. Score each point with distance to centroid
# 4. B = Select the smallest half of clusters
# 5. Build state 3 and 4 KD-Tree using top N-PC for each (from sampled PCA)
# 6. Run KMeans on each (???) for label/weight of H-Cubes in KD Tree (????)
# ALT-> use HCUbe size as its weight
# 7. A = HCubes for states 3 (and 4)
# 8. Reweight A into both state 3 and state 4 (B) HCubes
# 9. ID Overlap
# 10. Apply Gamme Function
logging.info("===== Covariance Matrix PCA-KMeans Calculation (B)")
logging.info("Retrieving All Covariance Vectors")
home = os.getenv('HOME')
cfile = home + '/work/DEBUG_COVAR_PTS'
DO_COVAR = self.calc_covar # For recalculating covariance matrices (if not pre-calc/stored)
if DO_COVAR:
if os.path.exists(cfile + '.npy'):
covar_pts = np.load(cfile + '.npy')
logging.debug('Loaded From File')
else:
covar_raw = self.catalog.lrange('subspace:covar:pts', 0, -1)
covar_pts = np.array([np.fromstring(x) for x in covar_raw])
np.save(cfile, covar_pts)
logging.debug('Loaded From Catalog & Saved')
covar_index = self.catalog.lrange('subspace:covar:xid', 0, -1)
logging.debug('Indiced Loaded. Retrieving File Indices')
covar_fidx = self.catalog.lrange('subspace:covar:fidx', 0, -1)
if DO_COVAR:
logging.info(" Pulled %d Covariance Vectors", len(covar_pts))
logging.info(
"Calculating Incremental PCA on Covariance (or Pick your PCA Algorithm here)"
)
# FOR incrementatl PCA:
NUM_PC = 6
ipca_key = 'subspace:covar:ipca'
ipca = PCAnalyzer.load(self.catalog, ipca_key)
if ipca is None:
logging.info('Creating a NEW IPCA')
ipca = PCAIncremental(NUM_PC)
lastindex = 0
else:
lastindex = ipca.trainsize
logging.info(
'IPCA Exists. Trained on %d pts. Will update with incremental batch of %d NEW pts',
ipca.trainsize,
len(covar_pts) - ipca.trainsize)
# For incremental, partial solve using only newer pts (from the last "trainsize")
if len(covar_pts) - lastindex > 0:
ipca.solve(covar_pts[lastindex:])
logging.info("Incrementatl PCA Updated. Storing Now...")
#### BARRIER
self.wait_catalog()
ipca.store(self.catalog, ipca_key)
logging.info("IPCA Saved. Projecting Covariance to PC")
cfile = home + '/work/DEBUG_SUBCOVAR_PTS'
if os.path.exists(cfile + '.npy'):
subspace_covar_pts = np.load(cfile + '.npy')
else:
subspace_covar_pts = ipca.project(covar_pts)
np.save(cfile, subspace_covar_pts)
# OW/ PROJECT NEW PTS ONLY -- BUT RETAIN grouped index of all points
logging.info(
'Building Global KD Tree over Covar Subspace with %d data pts',
len(subspace_covar_pts))
global_kdtree = KDTree(250,
maxdepth=8,
data=subspace_covar_pts,
method='middle')
if MAX_HCUBE <= 0:
hcube_global = global_kdtree.getleaves()
else:
# FOR DEBUGGING -- USE ONLY 3 GLOBAL HCUBES
hcube_global_ALL = global_kdtree.getleaves()
hcube_global = {}
num = 0
for k, v in hcube_global_ALL.items():
hcube_global[k] = v
num += 1
if num == MAX_HCUBE:
break
# hcube_global = global_kdtree.getleaves()
logging.info(
'Global HCubes: Key Count Volume Density (NOTE DEBUGGING ONLY 3 USED)'
)
for k in sorted(hcube_global.keys()):
v = hcube_global[k]
logging.info('%-10s %6d %8.1f %6.1f', k, v['count'],
v['volume'], v['density'])
if self.filelog:
keys = hcube_global.keys()
self.filelog.info('global,keys,%s', ','.join(keys))
self.filelog.info(
'global,count,%s',
','.join([str(hcube_global[k]['count']) for k in keys]))
self.filelog.info(
'global,volume,%s',
','.join([str(hcube_global[k]['volume']) for k in keys]))
self.filelog.info(
'global,density,%s',
','.join([str(hcube_global[k]['density']) for k in keys]))
logging.info(
"===== SELECT Sampling of points from each Global HCube (B)")
s = sorted(hcube_global.items(), key=lambda x: x[1]['count'])
hcube_global = {x[0]: x[1] for x in s}
counter = 0
for key in hcube_global.keys():
counter += 1
if hcube_global[key]['count'] <= MAX_SAMPLE_SIZE:
cov_index = hcube_global[key]['elm']
hcube_global[key]['samplefactor'] = 1
else:
cov_index = np.random.choice(hcube_global[key]['elm'],
MAX_SAMPLE_SIZE)
hcube_global[key]['samplefactor'] = len(
hcube_global[key]['elm']) / MAX_SAMPLE_SIZE
hcube_global[key]['idxlist'] = []
for cov in cov_index:
selected_hd_idx = np.random.choice(COVAR_SIZE,
MAX_PT_PER_MATRIX).tolist()
hcube_global[key]['idxlist'].extend(
[int(covar_index[cov]) + i for i in selected_hd_idx])
logging.info('Back Projecting Global HCube `%s` (%d out of %d)',
key, counter, len(hcube_global.keys()))
source_cov = self.backProjection(hcube_global[key]['idxlist'])
hcube_global[key]['alpha'] = datareduce.filter_alpha(source_cov)
logging.debug('Back Projected %d points to HD space: %s',
len(hcube_global[key]['idxlist']),
str(hcube_global[key]['alpha']))
# logging.info('Calculating all HD Distances')
# dist_hd = {}
# dist_ld = {}
# for key in hcube_global.keys():
# T = hcube_global[key]['alpha'].xyz
# N = len(T)
# dist_hd[key] = np.zeros(shape=(N, N))
# dist_ld[key] = {}
# for A in range(0, N):
# dist_hd[key][A][A] = 0
# for B in range(A+1, N):
# dist_hd[key][A][B] = dist_hd[key][B][A] = LA.norm(T[A] - T[B])
# KD Tree for states from Reservoir Sample of RMSD labeled HighDim
reservoir = ReservoirSample('rms', self.catalog)
logging.info(
"===== BUILD HCube Tree(s) Using Smallest State(s) (FROM RMSD Obsevations) "
)
hcube_list = {}
logging.info(
"Scanning current set of observed bins and finding all smallest with data (excluding largest 2)"
)
hcube_local = {}
logging.info("=======================================================")
logging.info(" PROJECT Global HCubes into Per-Bin HCube KD Tree(s)")
logging.info(
"=======================================================\n")
overlap_hcube = {k: {} for k in hcube_global.keys()}
projection_map = {}
pt_projection_list = []
for key in sorted(hcube_global.keys()):
for i in range(len(hcube_global[key]['alpha'].xyz)):
pt_projection_list.append([])
for bin_idx, tbin in enumerate(TEST_TBIN):
logging.info("Project Global HCubes into local subspace for %s",
str(tbin))
# Load Vectors
logging.info('Loading subspace and kernel for bin %s', str(tbin))
# LOAD KPCA Kernel matrix
kpca_key = 'subspace:pca:kernel:%d_%d' % tbin
kpca = PCAnalyzer.load(self.catalog, kpca_key)
data_raw = self.catalog.lrange('subspace:pca:%d_%d' % tbin, 0, -1)
data = np.array([np.fromstring(x) for x in data_raw])
if len(data) == 0:
logging.error(
'No Raw PCA data points for bin %s.... Going to next bin',
str(tbin))
continue
logging.info(
'Building KDtree over local %s bin from observations matrix of size: %s',
str(tbin), str(data.shape))
kdtree = KDTree(200, maxdepth=8, data=data, method='middle')
hcube_local[tbin] = kdtree.getleaves()
logging.info('LOCAL KD-Tree Completed for %s:', str(tbin))
for k in sorted(hcube_local[tbin].keys()):
logging.info(' `%-9s` #pts:%6d density:%9.1f', k,
len(hcube_local[tbin][k]['elm']),
hcube_local[tbin][k]['density'])
if self.filelog:
keys = hcube_local[tbin].keys()
A, B = tbin
self.filelog.info('local,%d_%d,keys,%s', A, B, ','.join(keys))
self.filelog.info(
'local,%d_%d,count,%s', A, B, ','.join(
[str(hcube_local[tbin][k]['count']) for k in keys]))
self.filelog.info(
'local,%d_%d,volume,%s', A, B, ','.join(
[str(hcube_local[tbin][k]['volume']) for k in keys]))
self.filelog.info(
'local,%d_%d,density,%s', A, B, ','.join(
[str(hcube_local[tbin][k]['density']) for k in keys]))
n_total = 0
logging.debug('Global Hcubes to Project (%d): %s',
len(hcube_global.keys()), str(hcube_global.keys()))
projection_map[bin_idx] = {
k: set()
for k in hcube_local[tbin].keys()
}
pnum = 0
for key in sorted(hcube_global.keys()):
overlap_hcube[key][tbin] = {}
cov_proj_pca = kpca.project(hcube_global[key]['alpha'].xyz)
logging.debug(
'PROJECT: Global HCube `%-9s` (%d pts) ==> Local KDTree %s ',
key, len(cov_proj_pca), str(tbin))
for i, pt in enumerate(cov_proj_pca):
hcube = kdtree.probe(pt, probedepth=9)
# NOTE: Retaining count of projected pts. Should we track individual pts -- YES (trying)
if hcube not in overlap_hcube[key][tbin]:
overlap_hcube[key][tbin][hcube] = {
'idxlist': hcube_local[tbin][hcube]['elm'],
'wgt': hcube_local[tbin][hcube]['density'],
'num_projected': 0
}
overlap_hcube[key][tbin][hcube]['num_projected'] += 1
# Index this point in corresponding local HCube projection view
projection_map[bin_idx][hcube].add(pnum)
pt_projection_list[pnum].append(hcube)
pnum += 1
for k, v in sorted(overlap_hcube[key][tbin].items()):
logging.debug(
' Project ==> Local HCube `%-9s`: %5d points', k,
v['num_projected'])
# logging.info('Calculating Lower Dimensional Distances')
# N = len(cov_proj_pca)
# dist_ld[key][tbin] = np.zeros(shape=(N, N))
# for A in range(0, N):
# for B in range(A+1, N):
# dist_ld[key][tbin][A][B] = dist_ld[key][tbin][B][A] = LA.norm(cov_proj_pca[A] - cov_proj_pca[B])
# Re-Index projected points -- could make this a list too
next_index = 0
view_list = []
for bin_idx, hcube_map in projection_map.items():
hcube_list = []
for hcube_key, pt_list in hcube_map.items():
hcube_list.append((set((hcube_key, )), set(pt_list)))
view_list.append((set((bin_idx, )), hcube_list))
print("CALLING: Collapse Join")
joined_subspaces = collapse_join(projection_map.keys(), view_list)
for subspace_list, correlated_hcubes in joined_subspaces:
tbin_list = [TEST_TBIN[bin_idx] for bin_idx in subspace_list]
for hcube_list, pt_list in correlated_hcubes:
print(tbin_list, hcube_list, pt_list)
# TODO: Corrlate Back to Global
print('Visualize HERE')
# for idx, tbin in enumerate(TEST_TBIN):
# # Only process substates with data
# if tbin not in hcube_local:
# logging.warning('Local KD Tree not created for %s', str(tbin))
# continue
# projection_map[(idx,)] = {k: set() for k in hcube_local[tbin].keys()}
# for n, proj in enumerate(pt_projection_list):
# for i, tbin in enumerate(proj_bin_list):
# sets[tbin][proj[i]].add(n)
# if self.filelog:
# self.filelog.info('%d,%s', n, ','.join(proj))
# logging.info('%d,%s', n, ','.join(proj))
# sets = {}
# proj_bin_list = []
# for tbin in TEST_TBIN:
# if tbin not in hcube_local:
# continue
# proj_bin_list.append(tbin)
# sets[tbin] = {k: set() for k in hcube_local[tbin].keys()}
# for n, proj in enumerate(pt_projection_list):
# for i, tbin in enumerate(proj_bin_list):
# sets[tbin][proj[i]].add(n)
# if self.filelog:
# self.filelog.info('%d,%s', n, ','.join(proj))
# logging.info('%d,%s', n, ','.join(proj))
# set_list = {}
# for tbin, view in sets.items():
# set_list[(tbin,)] = []
# for hcube, idxlist in view.items():
# print(tbin, hcube, idxlist)
# set_list[(tbin,)].append((set((hcube,)), idxlist))
# def collapse(C):
# a = 0
# b = 0
# N = []
# while a < len(C) and b < len(C):
# A = sorted(C[a])
# B = sorted(C[b])
# if A == B:
# b += 1
# elif A[0] == B[0]:
# N.append(set(A)|set(B))
# b += 1
# else:
# a += 1
# if len(N) <= 1:
# return []
# else:
# return N + collapse(N)
# q=collapse(t1)
# for i in q: print(sorted(i))
# print('Checking all 2-Way Joins')
# join2 = {}
# for a in range(0, len(proj_bin_list)-1):
# tA = proj_bin_list[a]
# for b in range(a+1, len(proj_bin_list)):
# tB = proj_bin_list[b]
# join_ss = tuple(set((tA, tB)))
# set_list = []
# for kA, vA in sets[tA].items():
# for kB, vB in sets[tB].items():
# join_hc = set((kA, kB))
# inter = vA & vB
# if len(inter) > 0:
# set_list.append((join_hc, inter))
# if len(set_list) > 0:
# join2[join_ss] = set_list
# print('2-Way Join Results:')
# for ss, set_list in join2.items():
# for hc, idxlist in set_list:
# print(ss, hc, idxlist)
# print('Checking all 3-Way Joins')
# join3 = []
# checked = []
# for a in range(0, len(join2)-1):
# sA, hA, vA = join2[a]
# for b in range(a+1, len(join2)):
# sB, hB, vB = join2[b]
# if sA == sB:
# continue
# ss, hc = sA | sB, hA | hB
# if (ss, hc) in checked[-10:]:
# continue
# checked.append((ss, hc))
# inter = vA & vB
# if len(inter) > 0:
# join3.append((ss, hc, inter))
# print('Checking all 4-Way Joins')
# join4 = []
# checked = []
# for a in range(0, len(join3)-1):
# sA, hA, vA = join3[a]
# for b in range(a+1, len(join3)):
# sB, hB, vB = join3[b]
# if sA == sB:
# continue
# ss, hc = sA | sB, hA | hB
# if (ss, hc) in checked[-10:]:
# continue
# checked.append((ss, hc))
# inter = vA & vB
# if len(inter) > 0:
# join4.append((ss, hc, inter))
# if self.filelog:
# for i in join2:
# self.filelog.info('%s', str(i))
# for i in join3:
# self.filelog.info('%s', str(i))
# for i in join4:
# self.filelog.info('%s', str(i))
DO_MIN_CHECK = False
if DO_MIN_CHECK:
def maxcount(x):
y = {}
for i in x:
y[i] = 1 if i not in y else y[i] + 1
return max(y.values())
print(
'%% of Points Per HCube with same NN subspaces (e.g. 20%% of points have same NN in 5 sub-spaces'
)
argmin_nonzero = lambda x: np.argmin([(i if i > 0 else np.inf)
for i in x])
for key in hcube_global.keys():
# logging.info('Showing MIN / MAX for points from HCube %s:', key)
minA = {}
maxA = {}
for n in range(len(dist_hd[key])):
minA[n] = []
maxA[n] = []
for tbin in TEST_TBIN:
if tbin not in dist_ld[key].keys():
continue
minA[n].append(0)
maxA[n].append(0)
else:
minA[n].append(
argmin_nonzero(dist_ld[key][tbin][n]))
maxA[n].append(np.argmax(dist_ld[key][tbin][n]))
numsame = np.zeros(len(dist_ld[key].keys()) + 1)
for n in range(len(dist_hd[key][n])):
minH = argmin_nonzero(dist_hd[key][n])
maxH = np.argmax(dist_hd[key][n])
minmax = ['%2d/%-2d' % i for i in zip(minA[n], maxA[n])]
numsamepair = maxcount(minA[n])
numsame[numsamepair] += 1
# print('%3d'%n, '%2d/%-2d '%(minH, maxH), '%s' % ' '.join(minmax), ' [%d]'%numsamepair)
print(' '.join([
'%4.1f%%' % i for i in (100 * (numsame / np.sum(numsame)))
]))
print('Stopping HERE!')
sys.exit(0)
# GAMMA FUNCTION EXPR # 8
gamma1 = lambda a, b: (a * b)
gamma2 = lambda a, b: (a + b) / 2
# TODO: Factor in RMS weight
for tbin in TEST_TBIN:
# for tbin in sorted(bin_list):
logging.info('')
logging.info('BIPARTITE GRAPH for %s', str(tbin))
bipart = {}
edgelist = []
for hcB in hcube_global.keys():
num_B = hcube_global[hcB]['count']
wgt1_B = hcube_global[hcB]['density']
if tbin not in overlap_hcube[hcB]:
continue
for hcA, hcA_data in overlap_hcube[hcB][tbin].items():
edge = {}
if hcA not in bipart:
bipart[hcA] = []
num_proj = hcA_data['num_projected']
wgt_A = hcA_data['wgt']
wgt2_B = wgt1_B * num_proj
edge['combW1'] = gamma1(wgt_A, wgt1_B)
edge['combW2'] = gamma1(wgt_A, wgt2_B)
edge['combW3'] = gamma2(wgt_A, wgt1_B)
edge['combW4'] = gamma2(wgt_A, wgt2_B)
edge['num_A'] = len(hcA_data['idxlist'])
edge['num_B'] = num_B
edge['num_proj'] = num_proj
edge['wgt_A'] = wgt_A
edge['wgt1_B'] = wgt1_B
edge['wgt2_B'] = wgt2_B
edge['hcA'] = hcA
edge['hcB'] = hcB
bipart[hcA].append(edge)
edgelist.append((hcA, hcB, num_proj))
if len(bipart) == 0:
logging.info("NO DATA FOR %s", str(tbin))
continue
logging.info('')
logging.info(
'A (# Pts) H-Cube <--- B H-Cube (# proj/total Pts) wgt_A wB1:density wB2:Mass A*B1 A*B2 AVG(A,B1) AVG(A,B2)'
)
for k, v in bipart.items():
for edge in v:
logging.info(
'A (%(num_A)4d pts) `%(hcA)-8s` <--- `%(hcB)9s` (%(num_B)4d / %(num_proj)4d pts) B %(wgt_A)9.1f %(wgt1_B)9.1f %(wgt2_B)9.1f %(combW1)9.1f %(combW2)9.1f %(combW3)9.1f %(combW3)9.1f'
% edge)
if self.filelog:
A, B = tbin
self.filelog.info('edge,%d_%d,%s,%s,%d', A, B,
edge['hcA'], edge['hcB'],
edge['num_proj'])
# Prepare nodes for graph
nA = set()
nB = set()
elist = []
for e in edgelist:
a, b, z = e
if z <= 5:
continue
nA.add(a)
nB.add(b)
elist.append((a, b, z))
nAKeys = sorted(nA)[::-1]
nBKeys = sorted(nB)[::-1]
sizesA = [hcube_local[tbin][n]['count'] for n in nAKeys]
sizesB = [hcube_global[n]['count'] * 3 for n in nBKeys]
idxA = {key: i for i, key in enumerate(nAKeys)}
idxB = {key: i for i, key in enumerate(nBKeys)}
edges = [(idxA[a], idxB[b], z) for a, b, z in elist]
G.bipartite(sizesA, sizesB, edges, sizesA, sizesB,
'bipartite_%d_%d' % tbin)
logging.info('STOPPING HERE!!!!')
sys.exit(0)
return []
def execute(self):
"""Special execute function for the reweight operator -- check/validate.
"""
# PRE-PROCESSING ---------------------------------------------------------------------------------
logging.debug("============================ <PRE-PROCESS> =============================")
self.cacheclient = CacheClient(self.name)
numLabels = 5
binlist = [(A, B) for A in range(numLabels) for B in range(numLabels)]
labeled_pts_rms = self.catalog.lrange('label:rms', 0, -1)
num_pts = len(labeled_pts_rms)
logging.debug('##NUM_OBS: %d', num_pts)
# TEST_TBIN = [(i,j) for i in range(2,5) for j in range(5)]
TEST_TBIN = [(2,0), (4,2), (2,2), (4,1), (3,1), (4,4), (0,4), (0,2), (0,1)]
MAX_SAMPLE_SIZE = 100 # Max # of cov traj to back project per HCube
MAX_PT_PER_MATRIX = 100 # Num points to sample from each cov traj
COVAR_SIZE = 200 # Ea Cov "pt" is 200 HD pts. -- should be static based on user query
MAX_HCUBE = 6 # Max Num HCubes to process
# IMPLEMENT USER QUERY with REWEIGHTING:
logging.debug("======================= <QUERY PROCESSING> =========================")
# 1. RUN KPCA on <<?????>> (sample set) and project all pts
# 2. Calculate K-D Tree on above
# 3. Score each point with distance to centroid
# 4. B = Select the smallest half of clusters
# 5. Build state 3 and 4 KD-Tree using top N-PC for each (from sampled PCA)
# 6. Run KMeans on each (???) for label/weight of H-Cubes in KD Tree (????)
# ALT-> use HCUbe size as its weight
# 7. A = HCubes for states 3 (and 4)
# 8. Reweight A into both state 3 and state 4 (B) HCubes
# 9. ID Overlap
# 10. Apply Gamme Function
logging.info("===== Covariance Matrix PCA-KMeans Calculation (B)")
logging.info("Retrieving All Covariance Vectors")
home = os.getenv('HOME')
cfile = home + '/work/DEBUG_COVAR_PTS'
DO_COVAR = self.calc_covar # For recalculating covariance matrices (if not pre-calc/stored)
if DO_COVAR:
if os.path.exists(cfile + '.npy'):
covar_pts = np.load(cfile + '.npy')
logging.debug('Loaded From File')
else:
covar_raw = self.catalog.lrange('subspace:covar:pts', 0, -1)
covar_pts = np.array([np.fromstring(x) for x in covar_raw])
np.save(cfile, covar_pts)
logging.debug('Loaded From Catalog & Saved')
covar_index = self.catalog.lrange('subspace:covar:xid', 0, -1)
logging.debug('Indiced Loaded. Retrieving File Indices')
covar_fidx = self.catalog.lrange('subspace:covar:fidx', 0, -1)
if DO_COVAR:
logging.info(" Pulled %d Covariance Vectors", len(covar_pts))
logging.info("Calculating Incremental PCA on Covariance (or Pick your PCA Algorithm here)")
# FOR incrementatl PCA:
NUM_PC = 6
ipca_key = 'subspace:covar:ipca'
ipca = PCAnalyzer.load(self.catalog, ipca_key)
if ipca is None:
logging.info('Creating a NEW IPCA')
ipca = PCAIncremental(NUM_PC)
lastindex = 0
else:
lastindex = ipca.trainsize
logging.info('IPCA Exists. Trained on %d pts. Will update with incremental batch of %d NEW pts',
ipca.trainsize, len(covar_pts)-ipca.trainsize)
# For incremental, partial solve using only newer pts (from the last "trainsize")
if len(covar_pts)-lastindex > 0:
ipca.solve(covar_pts[lastindex:])
logging.info("Incrementatl PCA Updated. Storing Now...")
#### BARRIER
self.wait_catalog()
ipca.store(self.catalog, ipca_key)
logging.info("IPCA Saved. Projecting Covariance to PC")
cfile = home + '/work/DEBUG_SUBCOVAR_PTS'
if os.path.exists(cfile + '.npy'):
subspace_covar_pts = np.load(cfile + '.npy')
else:
subspace_covar_pts = ipca.project(covar_pts)
np.save(cfile, subspace_covar_pts)
# OW/ PROJECT NEW PTS ONLY -- BUT RETAIN grouped index of all points
logging.info('Building Global KD Tree over Covar Subspace with %d data pts', len(subspace_covar_pts))
global_kdtree = KDTree(250, maxdepth=8, data=subspace_covar_pts, method='middle')
if MAX_HCUBE <= 0:
hcube_global = global_kdtree.getleaves()
else:
# FOR DEBUGGING -- USE ONLY 3 GLOBAL HCUBES
hcube_global_ALL = global_kdtree.getleaves()
hcube_global = {}
num = 0
for k, v in hcube_global_ALL.items():
hcube_global[k] = v
num += 1
if num == MAX_HCUBE:
break
# hcube_global = global_kdtree.getleaves()
logging.info('Global HCubes: Key Count Volume Density (NOTE DEBUGGING ONLY 3 USED)')
for k in sorted(hcube_global.keys()):
v = hcube_global[k]
logging.info('%-10s %6d %8.1f %6.1f', k, v['count'], v['volume'], v['density'])
if self.filelog:
keys = hcube_global.keys()
self.filelog.info('global,keys,%s',','.join(keys))
self.filelog.info('global,count,%s',','.join([str(hcube_global[k]['count']) for k in keys]))
self.filelog.info('global,volume,%s',','.join([str(hcube_global[k]['volume']) for k in keys]))
self.filelog.info('global,density,%s',','.join([str(hcube_global[k]['density']) for k in keys]))
logging.info("===== SELECT Sampling of points from each Global HCube (B)")
s = sorted(hcube_global.items(), key=lambda x: x[1]['count'])
hcube_global = {x[0]: x[1] for x in s}
counter = 0
for key in hcube_global.keys():
counter += 1
if hcube_global[key]['count'] <= MAX_SAMPLE_SIZE:
cov_index = hcube_global[key]['elm']
hcube_global[key]['samplefactor'] = 1
else:
cov_index = np.random.choice(hcube_global[key]['elm'], MAX_SAMPLE_SIZE)
hcube_global[key]['samplefactor'] = len(hcube_global[key]['elm']) / MAX_SAMPLE_SIZE
hcube_global[key]['idxlist'] = []
for cov in cov_index:
selected_hd_idx = np.random.choice(COVAR_SIZE, MAX_PT_PER_MATRIX).tolist()
hcube_global[key]['idxlist'].extend([int(covar_index[cov]) + i for i in selected_hd_idx])
logging.info('Back Projecting Global HCube `%s` (%d out of %d)', key, counter, len(hcube_global.keys()))
source_cov = self.backProjection(hcube_global[key]['idxlist'])
hcube_global[key]['alpha'] = datareduce.filter_alpha(source_cov)
logging.debug('Back Projected %d points to HD space: %s',
len(hcube_global[key]['idxlist']), str(hcube_global[key]['alpha']))
# logging.info('Calculating all HD Distances')
# dist_hd = {}
# dist_ld = {}
# for key in hcube_global.keys():
# T = hcube_global[key]['alpha'].xyz
# N = len(T)
# dist_hd[key] = np.zeros(shape=(N, N))
# dist_ld[key] = {}
# for A in range(0, N):
# dist_hd[key][A][A] = 0
# for B in range(A+1, N):
# dist_hd[key][A][B] = dist_hd[key][B][A] = LA.norm(T[A] - T[B])
# KD Tree for states from Reservoir Sample of RMSD labeled HighDim
reservoir = ReservoirSample('rms', self.catalog)
logging.info("===== BUILD HCube Tree(s) Using Smallest State(s) (FROM RMSD Obsevations) ")
hcube_list = {}
logging.info("Scanning current set of observed bins and finding all smallest with data (excluding largest 2)")
hcube_local = {}
logging.info("=======================================================")
logging.info(" PROJECT Global HCubes into Per-Bin HCube KD Tree(s)")
logging.info("=======================================================\n")
overlap_hcube = {k: {} for k in hcube_global.keys()}
projection_map = {}
pt_projection_list = []
for key in sorted(hcube_global.keys()):
for i in range(len(hcube_global[key]['alpha'].xyz)):
pt_projection_list.append([])
for bin_idx, tbin in enumerate(TEST_TBIN):
logging.info("Project Global HCubes into local subspace for %s", str(tbin))
# Load Vectors
logging.info('Loading subspace and kernel for bin %s', str(tbin))
# LOAD KPCA Kernel matrix
kpca_key = 'subspace:pca:kernel:%d_%d' % tbin
kpca = PCAnalyzer.load(self.catalog, kpca_key)
data_raw = self.catalog.lrange('subspace:pca:%d_%d' % tbin, 0, -1)
data = np.array([np.fromstring(x) for x in data_raw])
if len(data) == 0:
logging.error('No Raw PCA data points for bin %s.... Going to next bin', str(tbin))
continue
logging.info('Building KDtree over local %s bin from observations matrix of size: %s', str(tbin), str(data.shape))
kdtree = KDTree(200, maxdepth=8, data=data, method='middle')
hcube_local[tbin] = kdtree.getleaves()
logging.info('LOCAL KD-Tree Completed for %s:', str(tbin))
for k in sorted(hcube_local[tbin].keys()):
logging.info(' `%-9s` #pts:%6d density:%9.1f',
k, len(hcube_local[tbin][k]['elm']), hcube_local[tbin][k]['density'])
if self.filelog:
keys = hcube_local[tbin].keys()
A,B = tbin
self.filelog.info('local,%d_%d,keys,%s',A,B,','.join(keys))
self.filelog.info('local,%d_%d,count,%s',A,B,','.join([str(hcube_local[tbin][k]['count']) for k in keys]))
self.filelog.info('local,%d_%d,volume,%s',A,B,','.join([str(hcube_local[tbin][k]['volume']) for k in keys]))
self.filelog.info('local,%d_%d,density,%s',A,B,','.join([str(hcube_local[tbin][k]['density']) for k in keys]))
n_total = 0
logging.debug('Global Hcubes to Project (%d): %s', len(hcube_global.keys()), str(hcube_global.keys()))
projection_map[bin_idx] = {k: set() for k in hcube_local[tbin].keys()}
pnum = 0
for key in sorted(hcube_global.keys()):
overlap_hcube[key][tbin] = {}
cov_proj_pca = kpca.project(hcube_global[key]['alpha'].xyz)
logging.debug('PROJECT: Global HCube `%-9s` (%d pts) ==> Local KDTree %s ',
key, len(cov_proj_pca), str(tbin))
for i, pt in enumerate(cov_proj_pca):
hcube = kdtree.probe(pt, probedepth=9)
# NOTE: Retaining count of projected pts. Should we track individual pts -- YES (trying)
if hcube not in overlap_hcube[key][tbin]:
overlap_hcube[key][tbin][hcube] = {
'idxlist': hcube_local[tbin][hcube]['elm'],
'wgt': hcube_local[tbin][hcube]['density'],
'num_projected': 0}
overlap_hcube[key][tbin][hcube]['num_projected'] += 1
# Index this point in corresponding local HCube projection view
projection_map[bin_idx][hcube].add(pnum)
pt_projection_list[pnum].append(hcube)
pnum += 1
for k, v in sorted(overlap_hcube[key][tbin].items()):
logging.debug(' Project ==> Local HCube `%-9s`: %5d points', k, v['num_projected'])
# logging.info('Calculating Lower Dimensional Distances')
# N = len(cov_proj_pca)
# dist_ld[key][tbin] = np.zeros(shape=(N, N))
# for A in range(0, N):
# for B in range(A+1, N):
# dist_ld[key][tbin][A][B] = dist_ld[key][tbin][B][A] = LA.norm(cov_proj_pca[A] - cov_proj_pca[B])
# Re-Index projected points -- could make this a list too
next_index = 0
view_list = []
for bin_idx, hcube_map in projection_map.items():
hcube_list = []
for hcube_key, pt_list in hcube_map.items():
hcube_list.append((set((hcube_key,)), set(pt_list)))
view_list.append((set((bin_idx,)), hcube_list))
print("CALLING: Collapse Join")
joined_subspaces = collapse_join(projection_map.keys(), view_list)
for subspace_list, correlated_hcubes in joined_subspaces:
tbin_list = [TEST_TBIN[bin_idx] for bin_idx in subspace_list]
for hcube_list, pt_list in correlated_hcubes:
print(tbin_list, hcube_list, pt_list)
# TODO: Corrlate Back to Global
print('Visualize HERE')
# for idx, tbin in enumerate(TEST_TBIN):
# # Only process substates with data
# if tbin not in hcube_local:
# logging.warning('Local KD Tree not created for %s', str(tbin))
# continue
# projection_map[(idx,)] = {k: set() for k in hcube_local[tbin].keys()}
# for n, proj in enumerate(pt_projection_list):
# for i, tbin in enumerate(proj_bin_list):
# sets[tbin][proj[i]].add(n)
# if self.filelog:
# self.filelog.info('%d,%s', n, ','.join(proj))
# logging.info('%d,%s', n, ','.join(proj))
# sets = {}
# proj_bin_list = []
# for tbin in TEST_TBIN:
# if tbin not in hcube_local:
# continue
# proj_bin_list.append(tbin)
# sets[tbin] = {k: set() for k in hcube_local[tbin].keys()}
# for n, proj in enumerate(pt_projection_list):
# for i, tbin in enumerate(proj_bin_list):
# sets[tbin][proj[i]].add(n)
# if self.filelog:
# self.filelog.info('%d,%s', n, ','.join(proj))
# logging.info('%d,%s', n, ','.join(proj))
# set_list = {}
# for tbin, view in sets.items():
# set_list[(tbin,)] = []
# for hcube, idxlist in view.items():
# print(tbin, hcube, idxlist)
# set_list[(tbin,)].append((set((hcube,)), idxlist))
# def collapse(C):
# a = 0
# b = 0
# N = []
# while a < len(C) and b < len(C):
# A = sorted(C[a])
# B = sorted(C[b])
# if A == B:
# b += 1
# elif A[0] == B[0]:
# N.append(set(A)|set(B))
# b += 1
# else:
# a += 1
# if len(N) <= 1:
# return []
# else:
# return N + collapse(N)
# q=collapse(t1)
# for i in q: print(sorted(i))
# print('Checking all 2-Way Joins')
# join2 = {}
# for a in range(0, len(proj_bin_list)-1):
# tA = proj_bin_list[a]
# for b in range(a+1, len(proj_bin_list)):
# tB = proj_bin_list[b]
# join_ss = tuple(set((tA, tB)))
# set_list = []
# for kA, vA in sets[tA].items():
# for kB, vB in sets[tB].items():
# join_hc = set((kA, kB))
# inter = vA & vB
# if len(inter) > 0:
# set_list.append((join_hc, inter))
# if len(set_list) > 0:
# join2[join_ss] = set_list
# print('2-Way Join Results:')
# for ss, set_list in join2.items():
# for hc, idxlist in set_list:
# print(ss, hc, idxlist)
# print('Checking all 3-Way Joins')
# join3 = []
# checked = []
# for a in range(0, len(join2)-1):
# sA, hA, vA = join2[a]
# for b in range(a+1, len(join2)):
# sB, hB, vB = join2[b]
# if sA == sB:
# continue
# ss, hc = sA | sB, hA | hB
# if (ss, hc) in checked[-10:]:
# continue
# checked.append((ss, hc))
# inter = vA & vB
# if len(inter) > 0:
# join3.append((ss, hc, inter))
# print('Checking all 4-Way Joins')
# join4 = []
# checked = []
# for a in range(0, len(join3)-1):
# sA, hA, vA = join3[a]
# for b in range(a+1, len(join3)):
# sB, hB, vB = join3[b]
# if sA == sB:
# continue
# ss, hc = sA | sB, hA | hB
# if (ss, hc) in checked[-10:]:
# continue
# checked.append((ss, hc))
# inter = vA & vB
# if len(inter) > 0:
# join4.append((ss, hc, inter))
# if self.filelog:
# for i in join2:
# self.filelog.info('%s', str(i))
# for i in join3:
# self.filelog.info('%s', str(i))
# for i in join4:
# self.filelog.info('%s', str(i))
DO_MIN_CHECK = False
if DO_MIN_CHECK:
def maxcount(x):
y={}
for i in x:
y[i] = 1 if i not in y else y[i]+1
return max(y.values())
print('%% of Points Per HCube with same NN subspaces (e.g. 20%% of points have same NN in 5 sub-spaces')
argmin_nonzero = lambda x: np.argmin([(i if i>0 else np.inf) for i in x])
for key in hcube_global.keys():
# logging.info('Showing MIN / MAX for points from HCube %s:', key)
minA = {}; maxA={}
for n in range(len(dist_hd[key])) :
minA[n]=[] ; maxA[n]=[]
for tbin in TEST_TBIN:
if tbin not in dist_ld[key].keys():
continue
minA[n].append(0)
maxA[n].append(0)
else:
minA[n].append(argmin_nonzero(dist_ld[key][tbin][n]))
maxA[n].append(np.argmax(dist_ld[key][tbin][n]))
numsame = np.zeros(len(dist_ld[key].keys())+1)
for n in range(len(dist_hd[key][n])):
minH = argmin_nonzero(dist_hd[key][n])
maxH = np.argmax(dist_hd[key][n])
minmax = ['%2d/%-2d'%i for i in zip(minA[n], maxA[n])]
numsamepair = maxcount(minA[n])
numsame[numsamepair] += 1
# print('%3d'%n, '%2d/%-2d '%(minH, maxH), '%s' % ' '.join(minmax), ' [%d]'%numsamepair)
print(' '.join(['%4.1f%%'%i for i in (100* (numsame/np.sum(numsame)))]))
print('Stopping HERE!')
sys.exit(0)
# GAMMA FUNCTION EXPR # 8
gamma1 = lambda a, b : (a * b)
gamma2 = lambda a, b : (a + b) / 2
# TODO: Factor in RMS weight
for tbin in TEST_TBIN:
# for tbin in sorted(bin_list):
logging.info('')
logging.info('BIPARTITE GRAPH for %s', str(tbin))
bipart = {}
edgelist = []
for hcB in hcube_global.keys():
num_B = hcube_global[hcB]['count']
wgt1_B = hcube_global[hcB]['density']
if tbin not in overlap_hcube[hcB]:
continue
for hcA, hcA_data in overlap_hcube[hcB][tbin].items():
edge = {}
if hcA not in bipart:
bipart[hcA] = []
num_proj = hcA_data['num_projected']
wgt_A = hcA_data['wgt']
wgt2_B = wgt1_B*num_proj
edge['combW1'] = gamma1(wgt_A, wgt1_B)
edge['combW2'] = gamma1(wgt_A, wgt2_B)
edge['combW3'] = gamma2(wgt_A, wgt1_B)
edge['combW4'] = gamma2(wgt_A, wgt2_B)
edge['num_A'] = len(hcA_data['idxlist'])
edge['num_B'] = num_B
edge['num_proj'] = num_proj
edge['wgt_A'] = wgt_A
edge['wgt1_B'] = wgt1_B
edge['wgt2_B'] = wgt2_B
edge['hcA'] = hcA
edge['hcB'] = hcB
bipart[hcA].append(edge)
edgelist.append((hcA, hcB, num_proj))
if len(bipart) == 0:
logging.info("NO DATA FOR %s", str(tbin))
continue
logging.info('')
logging.info('A (# Pts) H-Cube <--- B H-Cube (# proj/total Pts) wgt_A wB1:density wB2:Mass A*B1 A*B2 AVG(A,B1) AVG(A,B2)')
for k, v in bipart.items():
for edge in v:
logging.info('A (%(num_A)4d pts) `%(hcA)-8s` <--- `%(hcB)9s` (%(num_B)4d / %(num_proj)4d pts) B %(wgt_A)9.1f %(wgt1_B)9.1f %(wgt2_B)9.1f %(combW1)9.1f %(combW2)9.1f %(combW3)9.1f %(combW3)9.1f' % edge)
if self.filelog:
A,B = tbin
self.filelog.info('edge,%d_%d,%s,%s,%d',A,B,edge['hcA'],edge['hcB'],edge['num_proj'])
# Prepare nodes for graph
nA = set()
nB = set()
elist = []
for e in edgelist:
a, b, z = e
if z <= 5:
continue
nA.add(a)
nB.add(b)
elist.append((a,b,z))
nAKeys = sorted(nA)[::-1]
nBKeys = sorted(nB)[::-1]
sizesA = [hcube_local[tbin][n]['count'] for n in nAKeys]
sizesB = [hcube_global[n]['count']*3 for n in nBKeys]
idxA = {key: i for i, key in enumerate(nAKeys)}
idxB = {key: i for i, key in enumerate(nBKeys)}
edges = [(idxA[a], idxB[b], z) for a, b, z in elist]
G.bipartite(sizesA,sizesB,edges,sizesA,sizesB,'bipartite_%d_%d' % tbin)
logging.info('STOPPING HERE!!!!')
sys.exit(0)
return []
